Version 1
: Received: 7 June 2024 / Approved: 10 June 2024 / Online: 10 June 2024 (07:59:10 CEST)
How to cite:
Sokač, M.; Mršić, L.; Balković, M.; Brkljačić, M. Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for EEG-Based Image Generation. Preprints2024, 2024060549. https://doi.org/10.20944/preprints202406.0549.v1
Sokač, M.; Mršić, L.; Balković, M.; Brkljačić, M. Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for EEG-Based Image Generation. Preprints 2024, 2024060549. https://doi.org/10.20944/preprints202406.0549.v1
Sokač, M.; Mršić, L.; Balković, M.; Brkljačić, M. Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for EEG-Based Image Generation. Preprints2024, 2024060549. https://doi.org/10.20944/preprints202406.0549.v1
APA Style
Sokač, M., Mršić, L., Balković, M., & Brkljačić, M. (2024). Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for EEG-Based Image Generation. Preprints. https://doi.org/10.20944/preprints202406.0549.v1
Chicago/Turabian Style
Sokač, M., Mislav Balković and Maja Brkljačić. 2024 "Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for EEG-Based Image Generation" Preprints. https://doi.org/10.20944/preprints202406.0549.v1
Abstract
Recent advancements in cognitive neuroscience, particularly in Electroencephalogram (EEG) signal processing, image generation, and brain-computer interfaces (BCI), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of AI to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected is then processed through an LSTM-based network employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain-computer interfaces.
Keywords
machine learning; EEG; image generation; EEG to image
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
